Machine learning teaches telcos customer retention tricks

03 MAY 2019

PARTNER CONTENT: Rahul Subramaniam, head of ZephyrTel’s Innovation Hub in Dubai, tipped predictive analytics as a key tool to help solve two of the most pressing issues facing operators today: figuring out how to improve customer experience and lower the total cost of ownership (TCO).

Using predictive analytics and machine learning (ML), Subramaniam said operators can take a proactive rather than reactive approach in the market, addressing customer dissatisfaction before it leads to a costly churn event. The idea, he explained, is to use AI and ML to not only predict which customers are likely to churn, but also easily design a custom rate plan which can address their unique needs.

Whether they know it or not, Subramaniam noted operators are already sitting on a gold mine of untapped data, with access to a vast quantity of records related to customer accounts, usage and location. He said there is “huge potential” to process this information to yield actionable insights around churn, delinquency, behaviour modelling and credit scoring.

“To drive a good customer experience programme, you need data,” he said. “Without this data, the customer experience programme will almost always be handicapped.”

Overcoming challenges
Subramaniam said legacy processing techniques such as data mining and rule-based insights have existed for decades, but added ML and AI can supercharge these methods to deliver predictions rather than historical reports.

“The problem is that today user behaviour, services and technology are all changing so rapidly that none of these rule-based models can keep pace with them.”

ML offers exciting potential for operators, but Subramaniam noted it is “extremely hard and expensive” to extract those insights by starting from scratch. This is because the infrastructure costs for specialised computing and storage are typically very high, and powerful custom tools are required to sift through the mountain of data operators possess.

There are also expenses associated with hiring experts with the specialised knowledge to fine-tune the data sets and translate the results into actionable insights.

“Running the machine learning models is a very small part of the operation. Cleaning up the data, getting it right, getting it in shape to be actually able to do the analysis is extremely hard and expensive and you need a whole slew of tools to be able to go do that,” he explained.

Subramaniam pointed out cloud providers such as Amazon Web Services have already taken on much of this challenge and offered their technology stacks as ready-to-use platforms. These can host services from ZephyrTel and others, and drastically lower the barrier to entry.

“The awesomeness of this approach is…you don’t have to make huge capital investments up front trying to build all the infrastructure and the hardware.”

Actionable insightsSubramaniam said ZephyrTel has developed technology which relies on ML to create precise customer segmentation based on factors such as usage, transaction history and spend history.

For instance, he said its algorithms can home in on recency (how recently users conducted a transaction) and frequency (how often they are making transactions), as well as the monetary value of those exchanges. Another can focus on usage, examining what time of day users make calls, how many SMS messages they send, how much data they consume and which apps they use most.

By slicing the data in different ways, Subramaniam said operators can identify “huge holes in segments where you probably don’t offer either the right rate plans or the right incentives for people to be in those segments”. Pinpointing these gaps presents an opportunity to build a new rate plan to attract users in a given segment and prevent churn among existing users, he added.

This has huge implications for cost reduction as “it is well established that the cost of acquisition of a new customer is five-times more than retaining one,” Subramaniam said.

ZephyrTel’s initial suite includes a feature which crunches data to determine what the ideal personalised rate plan for users should be, taking into account their usage patterns as well as operator profitability standards. The tool can also optimise existing rate plans to meet customer needs, he said.

“You can increase your customer satisfaction because you really understand your segments,” he said. “It maintains profitability, it maintains the resource allocation constraints that exist, and it will keep the customer really satisfied because you are giving them the control to choose what they want to consume.”